code: def _maybe_run_with_interpreter(fn):
      maybe_interpreted_fn = fn
      if (
          isinstance(fn, torch.fx.GraphModule)
          and fx_traceback.has_preserved_node_meta()
      ):
          # Running graph with interpreter is needed for propagating the stack_trace
          def graph_with_interpreter(*args):
              with fx_traceback.preserve_node_meta():
                  return torch.fx.Interpreter(fn).run(*args)
  
          maybe_interpreted_fn = graph_with_interpreter
      return maybe_interpreted_fn
  
imports: from contextlib import contextmanager
  from dataclasses import dataclass
  import torch
  import torch._subclasses.functional_tensor
  import torch.fx.traceback
  import torch.utils._pytree
  from torch._C import DispatchKey
  from torch._C._functorch import _add_batch_dim, get_unwrapped, is_batchedtensor, maybe_get_bdim
  from torch._functorch.utils import exposed_in
  from torch._higher_order_ops.utils import autograd_not_implemented
  from torch._ops import HigherOrderOperator
  from torch._subclasses.fake_tensor import FakeTensorMode
  from torch.fx.experimental.proxy_tensor import disable_proxy_modes_tracing, make_fx, ProxyTorchDispatchMode, track_tensor_tree
  from torch.fx.passes.shape_prop import _extract_tensor_metadata
  from torch.multiprocessing.reductions import StorageWeakRef
  from torch.utils._python_dispatch import _get_current_dispatch_mode
label_desc: Use image node_id 3 for calling a global function with example usage: _maybe_run_with_interpreter(fn) and returns: maybe_interpreted_fn, torch